Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations13206
Missing cells70532
Missing cells (%)19.1%
Total size in memory2.6 MiB
Average record size in memory204.0 B

Variable types

Numeric8
Text20

Alerts

kingdom has constant value "ANIMALIA" Constant
phylum has constant value "CHORDATA" Constant
class has constant value "MAMMALIA" Constant
subspecies has 12222 (92.5%) missing values Missing
subpop has 13142 (99.5%) missing values Missing
source has 12332 (93.4%) missing values Missing
island has 6796 (51.5%) missing values Missing
tax_comm has 13110 (99.3%) missing values Missing
dist_comm has 12930 (97.9%) missing values Missing
generalisd is highly skewed (γ1 = 33.13231472) Skewed
SHAPE_Area is highly skewed (γ1 = 22.12229833) Skewed
generalisd has 13194 (99.9%) zeros Zeros

Reproduction

Analysis started2025-08-05 11:59:00.866860
Analysis finished2025-08-05 11:59:01.435901
Duration0.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id_no
Real number (ℝ)

Distinct2960
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10232176.81
Minimum18
Maximum272127456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:01.522633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile3330
Q110982
median18719
Q341280
95-th percentile86263590
Maximum272127456
Range272127438
Interquartile range (IQR)30298

Descriptive statistics

Standard deviation36442629.21
Coefficient of variation (CV)3.561571491
Kurtosis20.03643081
Mean10232176.81
Median Absolute Deviation (MAD)9664
Skewness4.273430553
Sum1.351261269 × 1011
Variance1.328065224 × 1015
MonotonicityNot monotonic
2025-08-05T11:59:01.657965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19754 248
 
1.9%
15951 182
 
1.4%
21152 178
 
1.3%
13562 152
 
1.2%
4038 142
 
1.1%
21311 132
 
1.0%
10128 100
 
0.8%
18729 94
 
0.7%
13904 88
 
0.7%
41688 88
 
0.7%
Other values (2950) 11802
89.4%
ValueCountFrequency (%)
18 2
 
< 0.1%
139 40
0.3%
140 6
 
< 0.1%
219 16
 
0.1%
263 2
 
< 0.1%
ValueCountFrequency (%)
272127456 2
< 0.1%
271004723 2
< 0.1%
271003649 2
< 0.1%
271003501 2
< 0.1%
271001943 2
< 0.1%
Distinct2960
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:01.883068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length25
Mean length18.40345298
Min length5

Characters and Unicode

Total characters243036
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLophostoma occidentalis
2nd rowPseudomys pilligaensis
3rd rowCapreolus pygargus
4th rowCapreolus pygargus
5th rowGerbillus henleyi
ValueCountFrequency (%)
pteropus 630
 
2.4%
hipposideros 406
 
1.5%
crocidura 352
 
1.3%
rhinolophus 308
 
1.2%
myotis 298
 
1.1%
miniopterus 296
 
1.1%
rousettus 270
 
1.0%
australis 268
 
1.0%
amplexicaudatus 248
 
0.9%
panthera 232
 
0.9%
Other values (3272) 23104
87.5%
2025-08-05T11:59:02.218153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 23436
 
9.6%
i 21128
 
8.7%
a 20760
 
8.5%
o 17362
 
7.1%
e 16262
 
6.7%
u 15986
 
6.6%
r 15726
 
6.5%
13206
 
5.4%
t 12382
 
5.1%
n 11278
 
4.6%
Other values (41) 75510
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 23436
 
9.6%
i 21128
 
8.7%
a 20760
 
8.5%
o 17362
 
7.1%
e 16262
 
6.7%
u 15986
 
6.6%
r 15726
 
6.5%
13206
 
5.4%
t 12382
 
5.1%
n 11278
 
4.6%
Other values (41) 75510
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 23436
 
9.6%
i 21128
 
8.7%
a 20760
 
8.5%
o 17362
 
7.1%
e 16262
 
6.7%
u 15986
 
6.6%
r 15726
 
6.5%
13206
 
5.4%
t 12382
 
5.1%
n 11278
 
4.6%
Other values (41) 75510
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 23436
 
9.6%
i 21128
 
8.7%
a 20760
 
8.5%
o 17362
 
7.1%
e 16262
 
6.7%
u 15986
 
6.6%
r 15726
 
6.5%
13206
 
5.4%
t 12382
 
5.1%
n 11278
 
4.6%
Other values (41) 75510
31.1%

presence
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.188853551
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-08-05T11:59:02.305912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8024479417
Coefficient of variation (CV)0.6749762751
Kurtosis20.90922142
Mean1.188853551
Median Absolute Deviation (MAD)0
Skewness4.578533686
Sum15700
Variance0.6439226992
MonotonicityNot monotonic
2025-08-05T11:59:02.385523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 12384
93.8%
3 272
 
2.1%
6 166
 
1.3%
4 164
 
1.2%
5 136
 
1.0%
2 84
 
0.6%
ValueCountFrequency (%)
1 12384
93.8%
2 84
 
0.6%
3 272
 
2.1%
4 164
 
1.2%
5 136
 
1.0%
ValueCountFrequency (%)
6 166
1.3%
5 136
1.0%
4 164
1.2%
3 272
2.1%
2 84
 
0.6%

origin
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.079963653
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-08-05T11:59:02.463953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4718151069
Coefficient of variation (CV)0.4368805428
Kurtosis51.88717661
Mean1.079963653
Median Absolute Deviation (MAD)0
Skewness6.894029075
Sum14262
Variance0.2226094951
MonotonicityNot monotonic
2025-08-05T11:59:02.543741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 12744
96.5%
3 220
 
1.7%
2 116
 
0.9%
5 82
 
0.6%
4 24
 
0.2%
6 20
 
0.2%
ValueCountFrequency (%)
1 12744
96.5%
2 116
 
0.9%
3 220
 
1.7%
4 24
 
0.2%
5 82
 
0.6%
ValueCountFrequency (%)
6 20
 
0.2%
5 82
 
0.6%
4 24
 
0.2%
3 220
1.7%
2 116
0.9%

seasonal
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.011055581
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-08-05T11:59:02.619768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2007909172
Coefficient of variation (CV)0.1985953305
Kurtosis355.5800038
Mean1.011055581
Median Absolute Deviation (MAD)0
Skewness18.7152515
Sum13352
Variance0.04031699244
MonotonicityNot monotonic
2025-08-05T11:59:02.700919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 13164
99.7%
5 28
 
0.2%
3 8
 
0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
1 13164
99.7%
3 8
 
0.1%
4 6
 
< 0.1%
5 28
 
0.2%
ValueCountFrequency (%)
5 28
 
0.2%
4 6
 
< 0.1%
3 8
 
0.1%
1 13164
99.7%
Distinct144
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:02.927894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length200
Median length4
Mean length8.428138725
Min length3

Characters and Unicode

Total characters111302
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIUCN
2nd rowIUCN
3rd rowIUCN
4th rowIUCN
5th rowIUCN
ValueCountFrequency (%)
iucn 12234
56.8%
ssc 1172
 
5.4%
group 1166
 
5.4%
specialist 1156
 
5.4%
small 1108
 
5.1%
mammal 1108
 
5.1%
j 160
 
0.7%
petersen 156
 
0.7%
wyatt 156
 
0.7%
and 134
 
0.6%
Other values (368) 2982
 
13.8%
2025-08-05T11:59:03.311429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 13664
12.3%
N 12384
11.1%
I 12338
11.1%
U 12288
11.0%
8326
 
7.5%
a 6202
 
5.6%
l 5256
 
4.7%
S 4906
 
4.4%
i 3520
 
3.2%
m 3452
 
3.1%
Other values (68) 28966
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 13664
12.3%
N 12384
11.1%
I 12338
11.1%
U 12288
11.0%
8326
 
7.5%
a 6202
 
5.6%
l 5256
 
4.7%
S 4906
 
4.4%
i 3520
 
3.2%
m 3452
 
3.1%
Other values (68) 28966
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 13664
12.3%
N 12384
11.1%
I 12338
11.1%
U 12288
11.0%
8326
 
7.5%
a 6202
 
5.6%
l 5256
 
4.7%
S 4906
 
4.4%
i 3520
 
3.2%
m 3452
 
3.1%
Other values (68) 28966
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 13664
12.3%
N 12384
11.1%
I 12338
11.1%
U 12288
11.0%
8326
 
7.5%
a 6202
 
5.6%
l 5256
 
4.7%
S 4906
 
4.4%
i 3520
 
3.2%
m 3452
 
3.1%
Other values (68) 28966
26.0%

yrcompiled
Real number (ℝ)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.114191
Minimum2008
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-08-05T11:59:03.405399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12008
median2016
Q32019
95-th percentile2024
Maximum2024
Range16
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.732413804
Coefficient of variation (CV)0.002846121551
Kurtosis-1.494816867
Mean2014.114191
Median Absolute Deviation (MAD)7
Skewness0.1463733289
Sum26598392
Variance32.86056802
MonotonicityNot monotonic
2025-08-05T11:59:03.497170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2008 5616
42.5%
2016 1770
 
13.4%
2020 1068
 
8.1%
2019 1066
 
8.1%
2024 816
 
6.2%
2017 812
 
6.1%
2021 564
 
4.3%
2018 476
 
3.6%
2023 320
 
2.4%
2015 284
 
2.2%
Other values (7) 414
 
3.1%
ValueCountFrequency (%)
2008 5616
42.5%
2009 28
 
0.2%
2010 20
 
0.2%
2011 56
 
0.4%
2012 102
 
0.8%
ValueCountFrequency (%)
2024 816
6.2%
2023 320
 
2.4%
2022 116
 
0.9%
2021 564
4.3%
2020 1068
8.1%
Distinct79
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:03.716325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length222
Median length53
Mean length50.87626836
Min length3

Characters and Unicode

Total characters671872
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIUCN (International Union for Conservation of Nature)
2nd rowIUCN (International Union for Conservation of Nature)
3rd rowIUCN (International Union for Conservation of Nature)
4th rowIUCN (International Union for Conservation of Nature)
5th rowIUCN (International Union for Conservation of Nature)
ValueCountFrequency (%)
iucn 12564
13.9%
of 11396
12.6%
conservation 11384
12.6%
for 11378
12.6%
nature 11362
12.6%
union 11362
12.6%
international 11360
12.6%
group 1278
 
1.4%
ssc 1234
 
1.4%
specialist 1228
 
1.4%
Other values (198) 5716
6.3%
2025-08-05T11:59:04.065751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 81100
12.1%
77032
11.5%
o 70410
10.5%
a 52144
 
7.8%
t 47778
 
7.1%
r 47778
 
7.1%
i 37878
 
5.6%
e 37000
 
5.5%
C 25360
 
3.8%
U 24130
 
3.6%
Other values (64) 171262
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 671872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 81100
12.1%
77032
11.5%
o 70410
10.5%
a 52144
 
7.8%
t 47778
 
7.1%
r 47778
 
7.1%
i 37878
 
5.6%
e 37000
 
5.5%
C 25360
 
3.8%
U 24130
 
3.6%
Other values (64) 171262
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 671872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 81100
12.1%
77032
11.5%
o 70410
10.5%
a 52144
 
7.8%
t 47778
 
7.1%
r 47778
 
7.1%
i 37878
 
5.6%
e 37000
 
5.5%
C 25360
 
3.8%
U 24130
 
3.6%
Other values (64) 171262
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 671872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 81100
12.1%
77032
11.5%
o 70410
10.5%
a 52144
 
7.8%
t 47778
 
7.1%
r 47778
 
7.1%
i 37878
 
5.6%
e 37000
 
5.5%
C 25360
 
3.8%
U 24130
 
3.6%
Other values (64) 171262
25.5%

subspecies
Text

Missing 

Distinct304
Distinct (%)30.9%
Missing12222
Missing (%)92.5%
Memory size103.3 KiB
2025-08-05T11:59:04.231246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length15
Mean length8.508130081
Min length1

Characters and Unicode

Total characters8372
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpusillus
2nd rowpusillus
3rd rowpusillus
4th rowpusillus
5th rowpusillus
ValueCountFrequency (%)
solomonis 52
 
5.1%
papuana 36
 
3.5%
calcaratus 32
 
3.1%
koopmani 22
 
2.1%
lynx 20
 
2.0%
brachyotis 20
 
2.0%
cristatus 16
 
1.6%
cor 12
 
1.2%
natalae 12
 
1.2%
ssp 12
 
1.2%
Other values (295) 790
77.1%
2025-08-05T11:59:04.509513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1032
12.3%
i 916
10.9%
s 868
10.4%
n 634
 
7.6%
e 568
 
6.8%
o 556
 
6.6%
r 524
 
6.3%
u 476
 
5.7%
l 430
 
5.1%
t 428
 
5.1%
Other values (21) 1940
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1032
12.3%
i 916
10.9%
s 868
10.4%
n 634
 
7.6%
e 568
 
6.8%
o 556
 
6.6%
r 524
 
6.3%
u 476
 
5.7%
l 430
 
5.1%
t 428
 
5.1%
Other values (21) 1940
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1032
12.3%
i 916
10.9%
s 868
10.4%
n 634
 
7.6%
e 568
 
6.8%
o 556
 
6.6%
r 524
 
6.3%
u 476
 
5.7%
l 430
 
5.1%
t 428
 
5.1%
Other values (21) 1940
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1032
12.3%
i 916
10.9%
s 868
10.4%
n 634
 
7.6%
e 568
 
6.8%
o 556
 
6.6%
r 524
 
6.3%
u 476
 
5.7%
l 430
 
5.1%
t 428
 
5.1%
Other values (21) 1940
23.2%

subpop
Text

Missing 

Distinct24
Distinct (%)37.5%
Missing13142
Missing (%)99.5%
Memory size103.3 KiB
2025-08-05T11:59:04.652642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length17
Mean length12.125
Min length1

Characters and Unicode

Total characters776
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWestern
2nd rowPeruvian/Northern Chilean
3rd rowEastern lake area
4th rowNorthern lake area
5th rowTianjun
ValueCountFrequency (%)
west 8
 
6.3%
african 8
 
6.3%
lake 8
 
6.3%
0 6
 
4.8%
do 6
 
4.8%
morena 4
 
3.2%
montes 4
 
3.2%
de 4
 
3.2%
toledo 4
 
3.2%
sierra 4
 
3.2%
Other values (32) 70
55.6%
2025-08-05T11:59:04.888134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 88
 
11.3%
e 78
 
10.1%
62
 
8.0%
r 52
 
6.7%
n 52
 
6.7%
i 50
 
6.4%
o 48
 
6.2%
l 36
 
4.6%
d 32
 
4.1%
t 28
 
3.6%
Other values (35) 250
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 88
 
11.3%
e 78
 
10.1%
62
 
8.0%
r 52
 
6.7%
n 52
 
6.7%
i 50
 
6.4%
o 48
 
6.2%
l 36
 
4.6%
d 32
 
4.1%
t 28
 
3.6%
Other values (35) 250
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 88
 
11.3%
e 78
 
10.1%
62
 
8.0%
r 52
 
6.7%
n 52
 
6.7%
i 50
 
6.4%
o 48
 
6.2%
l 36
 
4.6%
d 32
 
4.1%
t 28
 
3.6%
Other values (35) 250
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 88
 
11.3%
e 78
 
10.1%
62
 
8.0%
r 52
 
6.7%
n 52
 
6.7%
i 50
 
6.4%
o 48
 
6.2%
l 36
 
4.6%
d 32
 
4.1%
t 28
 
3.6%
Other values (35) 250
32.2%

source
Text

Missing 

Distinct297
Distinct (%)34.0%
Missing12332
Missing (%)93.4%
Memory size103.3 KiB
2025-08-05T11:59:05.098800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length254
Median length220
Mean length51.21052632
Min length3

Characters and Unicode

Total characters44758
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMallon & Budd 2011
2nd rowBunaian et al. 2001
3rd rowMallon & Budd 2011
4th rowHamerlynck pers. comm. 2011
5th rowMallon & Budd 2011
ValueCountFrequency (%)
et 296
 
4.3%
al 296
 
4.3%
and 234
 
3.4%
of 170
 
2.5%
in 108
 
1.6%
a 78
 
1.1%
72
 
1.1%
pers 70
 
1.0%
comm 70
 
1.0%
the 66
 
1.0%
Other values (1110) 5378
78.6%
2025-08-05T11:59:05.451664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5968
 
13.3%
a 3200
 
7.1%
e 2946
 
6.6%
n 2228
 
5.0%
i 2076
 
4.6%
o 2062
 
4.6%
t 1948
 
4.4%
r 1816
 
4.1%
. 1768
 
4.0%
s 1648
 
3.7%
Other values (86) 19098
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5968
 
13.3%
a 3200
 
7.1%
e 2946
 
6.6%
n 2228
 
5.0%
i 2076
 
4.6%
o 2062
 
4.6%
t 1948
 
4.4%
r 1816
 
4.1%
. 1768
 
4.0%
s 1648
 
3.7%
Other values (86) 19098
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5968
 
13.3%
a 3200
 
7.1%
e 2946
 
6.6%
n 2228
 
5.0%
i 2076
 
4.6%
o 2062
 
4.6%
t 1948
 
4.4%
r 1816
 
4.1%
. 1768
 
4.0%
s 1648
 
3.7%
Other values (86) 19098
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5968
 
13.3%
a 3200
 
7.1%
e 2946
 
6.6%
n 2228
 
5.0%
i 2076
 
4.6%
o 2062
 
4.6%
t 1948
 
4.4%
r 1816
 
4.1%
. 1768
 
4.0%
s 1648
 
3.7%
Other values (86) 19098
42.7%

island
Text

Missing 

Distinct1017
Distinct (%)15.9%
Missing6796
Missing (%)51.5%
Memory size103.3 KiB
2025-08-05T11:59:05.667862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length25
Mean length7.728237129
Min length1

Characters and Unicode

Total characters49538
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheju Island
2nd rowBioko
3rd rowSumatra
4th rowJava
5th rowBali
ValueCountFrequency (%)
new 284
 
3.6%
island 208
 
2.7%
guinea 196
 
2.5%
borneo 182
 
2.3%
sumatra 160
 
2.1%
sulawesi 154
 
2.0%
java 114
 
1.5%
madagascar 106
 
1.4%
luzon 74
 
0.9%
lanka 54
 
0.7%
Other values (1034) 6270
80.4%
2025-08-05T11:59:05.998557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7934
16.0%
n 3742
 
7.6%
i 3704
 
7.5%
o 3132
 
6.3%
e 3034
 
6.1%
r 2804
 
5.7%
u 2430
 
4.9%
l 2032
 
4.1%
s 1736
 
3.5%
t 1464
 
3.0%
Other values (64) 17526
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7934
16.0%
n 3742
 
7.6%
i 3704
 
7.5%
o 3132
 
6.3%
e 3034
 
6.1%
r 2804
 
5.7%
u 2430
 
4.9%
l 2032
 
4.1%
s 1736
 
3.5%
t 1464
 
3.0%
Other values (64) 17526
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7934
16.0%
n 3742
 
7.6%
i 3704
 
7.5%
o 3132
 
6.3%
e 3034
 
6.1%
r 2804
 
5.7%
u 2430
 
4.9%
l 2032
 
4.1%
s 1736
 
3.5%
t 1464
 
3.0%
Other values (64) 17526
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7934
16.0%
n 3742
 
7.6%
i 3704
 
7.5%
o 3132
 
6.3%
e 3034
 
6.1%
r 2804
 
5.7%
u 2430
 
4.9%
l 2032
 
4.1%
s 1736
 
3.5%
t 1464
 
3.0%
Other values (64) 17526
35.4%

tax_comm
Text

Missing 

Distinct42
Distinct (%)43.8%
Missing13110
Missing (%)99.3%
Memory size103.3 KiB
2025-08-05T11:59:06.232463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length248
Median length72
Mean length62.8125
Min length21

Characters and Unicode

Total characters6030
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermediate with C. cyclindricornis
2nd rowIntermediate with C. caucasica
3rd rowThis form was originally described as a distinct species, then lumped with Thomasomys cinereiventer without comment by Cabrera (1961). Voss (2003) showed that T. erro is indeed a valid species.
4th rowSometimes placed in the genus Akodon.
5th rowrecently elevated from subspecies status of P. auritus
ValueCountFrequency (%)
the 32
 
3.5%
of 26
 
2.8%
in 24
 
2.6%
a 24
 
2.6%
genus 20
 
2.2%
as 20
 
2.2%
al 16
 
1.7%
is 16
 
1.7%
placed 16
 
1.7%
but 16
 
1.7%
Other values (201) 712
77.2%
2025-08-05T11:59:06.592246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
836
13.9%
e 550
 
9.1%
s 444
 
7.4%
i 406
 
6.7%
o 340
 
5.6%
n 332
 
5.5%
a 330
 
5.5%
t 328
 
5.4%
r 284
 
4.7%
l 218
 
3.6%
Other values (58) 1962
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
836
13.9%
e 550
 
9.1%
s 444
 
7.4%
i 406
 
6.7%
o 340
 
5.6%
n 332
 
5.5%
a 330
 
5.5%
t 328
 
5.4%
r 284
 
4.7%
l 218
 
3.6%
Other values (58) 1962
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
836
13.9%
e 550
 
9.1%
s 444
 
7.4%
i 406
 
6.7%
o 340
 
5.6%
n 332
 
5.5%
a 330
 
5.5%
t 328
 
5.4%
r 284
 
4.7%
l 218
 
3.6%
Other values (58) 1962
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
836
13.9%
e 550
 
9.1%
s 444
 
7.4%
i 406
 
6.7%
o 340
 
5.6%
n 332
 
5.5%
a 330
 
5.5%
t 328
 
5.4%
r 284
 
4.7%
l 218
 
3.6%
Other values (58) 1962
32.5%

dist_comm
Text

Missing 

Distinct96
Distinct (%)34.8%
Missing12930
Missing (%)97.9%
Memory size103.3 KiB
2025-08-05T11:59:06.783362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length175
Median length91
Mean length36.42028986
Min length1

Characters and Unicode

Total characters10052
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Records from Mole NP in Ghana and Comoe in Ivory Coast.
2nd rowThis species occurs to the east of P. brevicauda as far as Brasilia (Patton).
3rd rowMap originally compiled in 2016. Same map was used for the 2023 reassessment.
4th rowThis locality is very approximate and comes from two specimens from the late 1800s that had no location assigned to them (Flannery, 1995; F. Bonaccorso, pers. comm.).
5th rowHeirisson Prong
ValueCountFrequency (%)
the 90
 
5.9%
distribution 62
 
4.1%
map 60
 
4.0%
was 46
 
3.0%
for 46
 
3.0%
general 34
 
2.2%
of 34
 
2.2%
in 34
 
2.2%
used 30
 
2.0%
same 30
 
2.0%
Other values (288) 1052
69.3%
2025-08-05T11:59:07.130170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1242
 
12.4%
e 810
 
8.1%
a 782
 
7.8%
i 670
 
6.7%
s 630
 
6.3%
o 594
 
5.9%
n 584
 
5.8%
t 568
 
5.7%
r 550
 
5.5%
l 324
 
3.2%
Other values (66) 3298
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1242
 
12.4%
e 810
 
8.1%
a 782
 
7.8%
i 670
 
6.7%
s 630
 
6.3%
o 594
 
5.9%
n 584
 
5.8%
t 568
 
5.7%
r 550
 
5.5%
l 324
 
3.2%
Other values (66) 3298
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1242
 
12.4%
e 810
 
8.1%
a 782
 
7.8%
i 670
 
6.7%
s 630
 
6.3%
o 594
 
5.9%
n 584
 
5.8%
t 568
 
5.7%
r 550
 
5.5%
l 324
 
3.2%
Other values (66) 3298
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1242
 
12.4%
e 810
 
8.1%
a 782
 
7.8%
i 670
 
6.7%
s 630
 
6.3%
o 594
 
5.9%
n 584
 
5.8%
t 568
 
5.7%
r 550
 
5.5%
l 324
 
3.2%
Other values (66) 3298
32.8%

generalisd
Real number (ℝ)

Skewed  Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009086778737
Minimum0
Maximum1
Zeros13194
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2025-08-05T11:59:07.212842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03013172628
Coefficient of variation (CV)33.15996478
Kurtosis1095.916251
Mean0.0009086778737
Median Absolute Deviation (MAD)0
Skewness33.13231472
Sum12
Variance0.0009079209289
MonotonicityNot monotonic
2025-08-05T11:59:07.287536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 13194
99.9%
1 12
 
0.1%
ValueCountFrequency (%)
0 13194
99.9%
1 12
 
0.1%
ValueCountFrequency (%)
1 12
 
0.1%
0 13194
99.9%

legend
Text

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:07.388580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length17
Mean length17.68620324
Min length7

Characters and Unicode

Total characters233564
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExtant (resident)
2nd rowExtant (resident)
3rd rowExtant (resident)
4th rowExtant (resident)
5th rowExtant (resident)
ValueCountFrequency (%)
extant 12740
46.0%
resident 12702
45.9%
462
 
1.7%
possibly 436
 
1.6%
extinct 300
 
1.1%
uncertain 272
 
1.0%
introduced 220
 
0.8%
presence 166
 
0.6%
reintroduced 116
 
0.4%
probably 84
 
0.3%
Other values (7) 184
 
0.7%
2025-08-05T11:59:07.602721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 39478
16.9%
n 26982
11.6%
e 26692
11.4%
14476
 
6.2%
i 14082
 
6.0%
s 13880
 
5.9%
r 13674
 
5.9%
d 13402
 
5.7%
a 13224
 
5.7%
E 13040
 
5.6%
Other values (21) 44634
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 233564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 39478
16.9%
n 26982
11.6%
e 26692
11.4%
14476
 
6.2%
i 14082
 
6.0%
s 13880
 
5.9%
r 13674
 
5.9%
d 13402
 
5.7%
a 13224
 
5.7%
E 13040
 
5.6%
Other values (21) 44634
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 233564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 39478
16.9%
n 26982
11.6%
e 26692
11.4%
14476
 
6.2%
i 14082
 
6.0%
s 13880
 
5.9%
r 13674
 
5.9%
d 13402
 
5.7%
a 13224
 
5.7%
E 13040
 
5.6%
Other values (21) 44634
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 233564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 39478
16.9%
n 26982
11.6%
e 26692
11.4%
14476
 
6.2%
i 14082
 
6.0%
s 13880
 
5.9%
r 13674
 
5.9%
d 13402
 
5.7%
a 13224
 
5.7%
E 13040
 
5.6%
Other values (21) 44634
19.1%

kingdom
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:07.675495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105648
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANIMALIA
2nd rowANIMALIA
3rd rowANIMALIA
4th rowANIMALIA
5th rowANIMALIA
ValueCountFrequency (%)
animalia 13206
100.0%
2025-08-05T11:59:07.831317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 39618
37.5%
I 26412
25.0%
N 13206
 
12.5%
M 13206
 
12.5%
L 13206
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 39618
37.5%
I 26412
25.0%
N 13206
 
12.5%
M 13206
 
12.5%
L 13206
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 39618
37.5%
I 26412
25.0%
N 13206
 
12.5%
M 13206
 
12.5%
L 13206
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 39618
37.5%
I 26412
25.0%
N 13206
 
12.5%
M 13206
 
12.5%
L 13206
 
12.5%

phylum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:07.896506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105648
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHORDATA
2nd rowCHORDATA
3rd rowCHORDATA
4th rowCHORDATA
5th rowCHORDATA
ValueCountFrequency (%)
chordata 13206
100.0%
2025-08-05T11:59:08.054002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 26412
25.0%
H 13206
12.5%
C 13206
12.5%
O 13206
12.5%
R 13206
12.5%
D 13206
12.5%
T 13206
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 26412
25.0%
H 13206
12.5%
C 13206
12.5%
O 13206
12.5%
R 13206
12.5%
D 13206
12.5%
T 13206
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 26412
25.0%
H 13206
12.5%
C 13206
12.5%
O 13206
12.5%
R 13206
12.5%
D 13206
12.5%
T 13206
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 26412
25.0%
H 13206
12.5%
C 13206
12.5%
O 13206
12.5%
R 13206
12.5%
D 13206
12.5%
T 13206
12.5%

class
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:08.119950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters105648
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAMMALIA
2nd rowMAMMALIA
3rd rowMAMMALIA
4th rowMAMMALIA
5th rowMAMMALIA
ValueCountFrequency (%)
mammalia 13206
100.0%
2025-08-05T11:59:08.277173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 39618
37.5%
A 39618
37.5%
L 13206
 
12.5%
I 13206
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 39618
37.5%
A 39618
37.5%
L 13206
 
12.5%
I 13206
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 39618
37.5%
A 39618
37.5%
L 13206
 
12.5%
I 13206
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 39618
37.5%
A 39618
37.5%
L 13206
 
12.5%
I 13206
 
12.5%

order_
Text

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:08.374702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.758897471
Min length6

Characters and Unicode

Total characters128876
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHIROPTERA
2nd rowRODENTIA
3rd rowARTIODACTYLA
4th rowARTIODACTYLA
5th rowRODENTIA
ValueCountFrequency (%)
chiroptera 4928
37.3%
rodentia 3322
25.2%
carnivora 1232
 
9.3%
primates 874
 
6.6%
artiodactyla 772
 
5.8%
eulipotyphla 766
 
5.8%
diprotodontia 422
 
3.2%
dasyuromorphia 170
 
1.3%
lagomorpha 162
 
1.2%
peramelemorphia 114
 
0.9%
Other values (14) 444
 
3.4%
2025-08-05T11:59:08.582456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 18808
14.6%
A 16724
13.0%
I 13740
10.7%
O 13512
10.5%
T 12500
9.7%
E 10564
8.2%
P 8612
6.7%
C 7250
 
5.6%
H 6380
 
5.0%
D 5596
 
4.3%
Other values (10) 15190
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 18808
14.6%
A 16724
13.0%
I 13740
10.7%
O 13512
10.5%
T 12500
9.7%
E 10564
8.2%
P 8612
6.7%
C 7250
 
5.6%
H 6380
 
5.0%
D 5596
 
4.3%
Other values (10) 15190
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 18808
14.6%
A 16724
13.0%
I 13740
10.7%
O 13512
10.5%
T 12500
9.7%
E 10564
8.2%
P 8612
6.7%
C 7250
 
5.6%
H 6380
 
5.0%
D 5596
 
4.3%
Other values (10) 15190
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 18808
14.6%
A 16724
13.0%
I 13740
10.7%
O 13512
10.5%
T 12500
9.7%
E 10564
8.2%
P 8612
6.7%
C 7250
 
5.6%
H 6380
 
5.0%
D 5596
 
4.3%
Other values (10) 15190
11.8%

family
Text

Distinct143
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:08.741307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length15
Mean length10.83234893
Min length6

Characters and Unicode

Total characters143052
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHYLLOSTOMIDAE
2nd rowMURIDAE
3rd rowCERVIDAE
4th rowCERVIDAE
5th rowMURIDAE
ValueCountFrequency (%)
pteropodidae 1500
 
11.4%
muridae 1286
 
9.7%
vespertilionidae 980
 
7.4%
cricetidae 832
 
6.3%
soricidae 618
 
4.7%
sciuridae 526
 
4.0%
felidae 522
 
4.0%
hipposideridae 474
 
3.6%
emballonuridae 442
 
3.3%
molossidae 410
 
3.1%
Other values (133) 5616
42.5%
2025-08-05T11:59:09.018660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 22526
15.7%
I 20836
14.6%
D 16014
11.2%
A 15732
11.0%
O 10564
7.4%
R 10092
7.1%
P 7694
 
5.4%
T 5784
 
4.0%
C 5514
 
3.9%
L 5324
 
3.7%
Other values (13) 22972
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 22526
15.7%
I 20836
14.6%
D 16014
11.2%
A 15732
11.0%
O 10564
7.4%
R 10092
7.1%
P 7694
 
5.4%
T 5784
 
4.0%
C 5514
 
3.9%
L 5324
 
3.7%
Other values (13) 22972
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 22526
15.7%
I 20836
14.6%
D 16014
11.2%
A 15732
11.0%
O 10564
7.4%
R 10092
7.1%
P 7694
 
5.4%
T 5784
 
4.0%
C 5514
 
3.9%
L 5324
 
3.7%
Other values (13) 22972
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 22526
15.7%
I 20836
14.6%
D 16014
11.2%
A 15732
11.0%
O 10564
7.4%
R 10092
7.1%
P 7694
 
5.4%
T 5784
 
4.0%
C 5514
 
3.9%
L 5324
 
3.7%
Other values (13) 22972
16.1%

genus
Text

Distinct958
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:09.217028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.909889444
Min length2

Characters and Unicode

Total characters117664
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLophostoma
2nd rowPseudomys
3rd rowCapreolus
4th rowCapreolus
5th rowGerbillus
ValueCountFrequency (%)
pteropus 630
 
4.8%
hipposideros 372
 
2.8%
crocidura 352
 
2.7%
rhinolophus 308
 
2.3%
miniopterus 296
 
2.2%
myotis 288
 
2.2%
rousettus 270
 
2.0%
panthera 232
 
1.8%
emballonura 218
 
1.7%
tadarida 188
 
1.4%
Other values (948) 10052
76.1%
2025-08-05T11:59:09.525850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 11868
 
10.1%
o 11508
 
9.8%
a 8370
 
7.1%
r 8248
 
7.0%
u 8042
 
6.8%
i 7906
 
6.7%
e 7758
 
6.6%
t 6202
 
5.3%
l 4808
 
4.1%
p 4680
 
4.0%
Other values (40) 38274
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 11868
 
10.1%
o 11508
 
9.8%
a 8370
 
7.1%
r 8248
 
7.0%
u 8042
 
6.8%
i 7906
 
6.7%
e 7758
 
6.6%
t 6202
 
5.3%
l 4808
 
4.1%
p 4680
 
4.0%
Other values (40) 38274
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 11868
 
10.1%
o 11508
 
9.8%
a 8370
 
7.1%
r 8248
 
7.0%
u 8042
 
6.8%
i 7906
 
6.7%
e 7758
 
6.6%
t 6202
 
5.3%
l 4808
 
4.1%
p 4680
 
4.0%
Other values (40) 38274
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 11868
 
10.1%
o 11508
 
9.8%
a 8370
 
7.1%
r 8248
 
7.0%
u 8042
 
6.8%
i 7906
 
6.7%
e 7758
 
6.6%
t 6202
 
5.3%
l 4808
 
4.1%
p 4680
 
4.0%
Other values (40) 38274
32.5%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:09.602025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26412
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNT
2nd rowDD
3rd rowLC
4th rowLC
5th rowLC
ValueCountFrequency (%)
lc 8154
61.7%
vu 1610
 
12.2%
nt 1100
 
8.3%
en 1056
 
8.0%
dd 908
 
6.9%
cr 350
 
2.7%
ex 24
 
0.2%
ew 4
 
< 0.1%
2025-08-05T11:59:09.751717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 8504
32.2%
L 8154
30.9%
N 2156
 
8.2%
D 1816
 
6.9%
V 1610
 
6.1%
U 1610
 
6.1%
T 1100
 
4.2%
E 1084
 
4.1%
R 350
 
1.3%
X 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 8504
32.2%
L 8154
30.9%
N 2156
 
8.2%
D 1816
 
6.9%
V 1610
 
6.1%
U 1610
 
6.1%
T 1100
 
4.2%
E 1084
 
4.1%
R 350
 
1.3%
X 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 8504
32.2%
L 8154
30.9%
N 2156
 
8.2%
D 1816
 
6.9%
V 1610
 
6.1%
U 1610
 
6.1%
T 1100
 
4.2%
E 1084
 
4.1%
R 350
 
1.3%
X 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 8504
32.2%
L 8154
30.9%
N 2156
 
8.2%
D 1816
 
6.9%
V 1610
 
6.1%
U 1610
 
6.1%
T 1100
 
4.2%
E 1084
 
4.1%
R 350
 
1.3%
X 24
 
0.1%

marine
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:09.816745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.986218386
Min length4

Characters and Unicode

Total characters65848
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfalse
2nd rowfalse
3rd rowfalse
4th rowfalse
5th rowfalse
ValueCountFrequency (%)
false 13024
98.6%
true 182
 
1.4%
2025-08-05T11:59:09.974986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13206
20.1%
f 13024
19.8%
l 13024
19.8%
a 13024
19.8%
s 13024
19.8%
t 182
 
0.3%
r 182
 
0.3%
u 182
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 13024
19.8%
l 13024
19.8%
a 13024
19.8%
s 13024
19.8%
t 182
 
0.3%
r 182
 
0.3%
u 182
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 13024
19.8%
l 13024
19.8%
a 13024
19.8%
s 13024
19.8%
t 182
 
0.3%
r 182
 
0.3%
u 182
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 13024
19.8%
l 13024
19.8%
a 13024
19.8%
s 13024
19.8%
t 182
 
0.3%
r 182
 
0.3%
u 182
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:10.034152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.009238225
Min length4

Characters and Unicode

Total characters52946
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrue
2nd rowtrue
3rd rowtrue
4th rowtrue
5th rowtrue
ValueCountFrequency (%)
true 13084
99.1%
false 122
 
0.9%
2025-08-05T11:59:10.191037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13206
24.9%
t 13084
24.7%
r 13084
24.7%
u 13084
24.7%
f 122
 
0.2%
a 122
 
0.2%
l 122
 
0.2%
s 122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13206
24.9%
t 13084
24.7%
r 13084
24.7%
u 13084
24.7%
f 122
 
0.2%
a 122
 
0.2%
l 122
 
0.2%
s 122
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13206
24.9%
t 13084
24.7%
r 13084
24.7%
u 13084
24.7%
f 122
 
0.2%
a 122
 
0.2%
l 122
 
0.2%
s 122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13206
24.9%
t 13084
24.7%
r 13084
24.7%
u 13084
24.7%
f 122
 
0.2%
a 122
 
0.2%
l 122
 
0.2%
s 122
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:10.253751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.982280781
Min length4

Characters and Unicode

Total characters65796
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfalse
2nd rowfalse
3rd rowfalse
4th rowfalse
5th rowfalse
ValueCountFrequency (%)
false 12972
98.2%
true 234
 
1.8%
2025-08-05T11:59:10.412482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13206
20.1%
f 12972
19.7%
l 12972
19.7%
a 12972
19.7%
s 12972
19.7%
t 234
 
0.4%
r 234
 
0.4%
u 234
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 12972
19.7%
l 12972
19.7%
a 12972
19.7%
s 12972
19.7%
t 234
 
0.4%
r 234
 
0.4%
u 234
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 12972
19.7%
l 12972
19.7%
a 12972
19.7%
s 12972
19.7%
t 234
 
0.4%
r 234
 
0.4%
u 234
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13206
20.1%
f 12972
19.7%
l 12972
19.7%
a 12972
19.7%
s 12972
19.7%
t 234
 
0.4%
r 234
 
0.4%
u 234
 
0.4%

SHAPE_Leng
Real number (ℝ)

Distinct6060
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.73319358
Minimum0.003332949744
Maximum13533.13674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:10.509836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.003332949744
5-th percentile0.2049183878
Q11.356394264
median6.628001834
Q336.7563408
95-th percentile245.9667287
Maximum13533.13674
Range13533.13341
Interquartile range (IQR)35.39994654

Descriptive statistics

Standard deviation373.8701697
Coefficient of variation (CV)5.361437653
Kurtosis443.4626404
Mean69.73319358
Median Absolute Deviation (MAD)6.207486882
Skewness18.00700813
Sum920896.5544
Variance139778.9038
MonotonicityNot monotonic
2025-08-05T11:59:10.639053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.906926095 34
 
0.3%
6.831416433 16
 
0.1%
1.995217593 14
 
0.1%
50.34793327 14
 
0.1%
1.441266148 14
 
0.1%
8.778680047 14
 
0.1%
5.483919325 14
 
0.1%
0.9030914056 12
 
0.1%
3.590023122 12
 
0.1%
2.819346066 12
 
0.1%
Other values (6050) 13050
98.8%
ValueCountFrequency (%)
0.003332949744 2
< 0.1%
0.01217756948 2
< 0.1%
0.01658862422 2
< 0.1%
0.01894410912 2
< 0.1%
0.02208800127 2
< 0.1%
ValueCountFrequency (%)
13533.13674 2
< 0.1%
8759.735027 2
< 0.1%
8255.401504 2
< 0.1%
7884.316331 2
< 0.1%
6960.250565 2
< 0.1%

SHAPE_Area
Real number (ℝ)

Skewed 

Distinct6063
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.4747172
Minimum8.839681965 × 10-7
Maximum38144.58799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.3 KiB
2025-08-05T11:59:10.768557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.839681965 × 10-7
5-th percentile0.001745557508
Q10.05010228839
median0.9094047241
Q317.41985984
95-th percentile345.4800208
Maximum38144.58799
Range38144.58799
Interquartile range (IQR)17.36975755

Descriptive statistics

Standard deviation1076.460958
Coefficient of variation (CV)9.403482129
Kurtosis571.9212215
Mean114.4747172
Median Absolute Deviation (MAD)0.9069572191
Skewness22.12229833
Sum1511753.116
Variance1158768.194
MonotonicityNot monotonic
2025-08-05T11:59:10.899569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4008237602 34
 
0.3%
0.9410492848 16
 
0.1%
0.708725095 14
 
0.1%
34.93508728 14
 
0.1%
0.5715578699 14
 
0.1%
0.0788077901 14
 
0.1%
10.31465133 14
 
0.1%
0.07534918 12
 
0.1%
0.1507804659 12
 
0.1%
0.02579748003 12
 
0.1%
Other values (6053) 13050
98.8%
ValueCountFrequency (%)
8.839681965 × 10-72
< 0.1%
9.009396273 × 10-62
< 0.1%
2.136451668 × 10-52
< 0.1%
2.252516381 × 10-52
< 0.1%
2.6909631 × 10-52
< 0.1%
ValueCountFrequency (%)
38144.58799 2
< 0.1%
32108.87995 2
< 0.1%
25055.23832 2
< 0.1%
23292.23264 2
< 0.1%
22662.53968 2
< 0.1%